Physics Data Processing - The online connection

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This presentation contains an overview of my work at Nikhef, the Dutch national institute for subatomic physics, in the last few years.

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Physics Data Processing - The online connection

  1. 1. Physics Data Processing -The online connection<br />Nikhef colloquium<br />9/2/2009<br />Sander Klous<br />
  2. 2. The steamboat view<br />PDP Activities<br />Bob Hertzberger<br />2<br />Physics Data Processing - Sander Klous<br />9/2/2009<br />Remote Online Farms<br />Use Cases (i.e. trigger performance)<br />My focus<br />
  3. 3. Remote Online Farms<br />What is a remote online farm?<br />The connection to the online system<br />Event routing and streaming<br />Remote online farms in ATLAS (Hegoi Garitaonandia)<br />Debug stream reprocessing<br />Trigger menu validation<br />Muon calibration stream<br />Do we really need remote online farms?<br />9/2/2009<br />Physics Data Processing - Sander Klous<br />3<br />
  4. 4. Concept<br />Physics Data Processing - Sander Klous<br />4<br />Data Acquisition<br />40 MHz<br />Level 1<br />Level 2<br />Level 3<br />Amsterdam<br />Accept 1 in 500<br />NIKHEF/SARA<br />Network<br />switch<br />Accept 1 in 50<br />Accept 1 in 10<br />Computing grid<br />9/2/2009<br />
  5. 5. Why is this interesting?<br />9/2/2009<br />Physics Data Processing - Sander Klous<br />5<br />January 2001<br />Gary Stix, editor of Scientific American<br />
  6. 6. Connection to the online system<br />9/2/2009<br />Physics Data Processing - Sander Klous<br />6<br />ROF<br />ROF<br />ROF<br />ROF<br />EF<br />SFO<br />Remote Event Processing Farms<br />ROB<br />ROB<br />ROB<br />ROB<br />Data Collection Network<br />Packet<br />Switched<br />(GEANT)<br />SFI<br />SFI<br />SFI<br />Routing and Streaming<br />Level 2 Trigger<br />Light path<br />Back End Network<br />L2PU<br />Event Filter<br />Mass<br />storage<br />Building 513<br />CatalinMeirosu<br />DDM<br />Local Farm<br />
  7. 7. Routing and Streaming introduction<br />9/2/2009<br />Physics Data Processing - Sander Klous<br />7<br />Hans von der Schmitt<br />Optimizing resources<br />Online, different routes<br />Offline, different streams<br />Classification of events<br />Physics<br />Calibration<br />Debug<br />Express<br />Remote<br />
  8. 8. Regions of Interest andPartial Event Building<br />9/2/2009<br />Physics Data Processing - Sander Klous<br />8<br />Virtual Point 1<br />Ignacio Aracena<br />
  9. 9. Inclusive or Exclusive streaming<br />9/2/2009<br />Physics Data Processing - Sander Klous<br />9<br />Hans von der Schmitt<br />
  10. 10. Online monitoring of overlaps<br />9/2/2009<br />Physics Data Processing - Sander Klous<br />10<br />Brian Petersen<br />
  11. 11. Luminosity blocks<br />9/2/2009<br />Physics Data Processing - Sander Klous<br />11<br />Hans von der Schmitt<br />
  12. 12. Final Dress RehearsalData Quality Monitoring<br />DDM/DQ2<br />TIER-0<br />RAW<br />320<br />TIER-1s<br />320<br />ESD<br />720<br />200<br />200<br />200<br />CASTOR<br />AOD<br />200<br />720<br />TAG<br />440<br />Luc Goossens<br />CPU farm<br />Data<br />acquisition<br />TAG<br />DB<br />disk<br />150TB<br />1060<br />raid<br />25TB<br />load TAG<br />320<br />130<br />recon EXPR<br />340<br />online<br />SFO<br />recon PHYS<br />T0M<br />DB<br />c1<br />Cond<br />DB<br />calib/align<br />c1<br />Express stream handling<br />9/2/2009<br />Physics Data Processing - Sander Klous<br />12<br />Rates in MB/s<br />
  13. 13. Debug stream reprocessing<br />9/2/2009<br />Physics Data Processing - Sander Klous<br />13<br />
  14. 14. Trigger menu validation<br />9/2/2009<br />Physics Data Processing - Sander Klous<br />14<br />
  15. 15. Muon calibration<br />9/2/2009<br />Physics Data Processing - Sander Klous<br />15<br />MuonCalibrationGroup<br />Virtual Point 1<br />
  16. 16. Do we really need ROFs?<br />At the moment: No, not really…<br />Not for physics at least<br />Transition will be gradual<br />Example: debug stream reprocessing<br />Limited resources in the CERN Analysis Facility<br />Funding will play an important role<br />Easier to fund online resources in home country<br />Other factors: energy consumption, human resources<br />What will be the first physics use case?<br />9/2/2009<br />Physics Data Processing - Sander Klous<br />16<br />
  17. 17. Trigger performance<br />Fully Hadronic decays of top pairs<br />The jet trigger challenge<br />Determining trigger efficiencies from data<br />Tag and probe with leptons<br />T&P with semi-leptonic decays of top pairs(Menelaos Tsiakiris)<br />Turning the probe around, T&P with jets<br />Jet trigger efficiencies and rates<br />Beyond multi-jet triggers<br />Topology triggers and remote online farms<br />9/2/2009<br />Physics Data Processing - Sander Klous<br />17<br />
  18. 18. Fully Hadronic decaysof top-antitop pairs<br />9/2/2009<br />Physics Data Processing - Sander Klous<br />18<br />
  19. 19. The jet trigger challenge<br />QCD 6 jet background<br />1000 to 10000 x Signal<br />Is this possible at all?<br />Long term study<br />First look at semi-leptonic decays<br />Study the possibility to trigger on jets<br />Extract jet trigger efficiency from data<br />Cross correlate with muon triggers<br />9/2/2009<br />Physics Data Processing - Sander Klous<br />19<br />Turn on kicks in.<br />Trigger problem<br />Lambacher<br />Munich<br />O(1 fb-1)<br />
  20. 20. Trigger efficiency from data<br />20 GeV muon turn on curve (CSC note)<br />Tagged<br />Tagged and Probed<br />Tag and probe<br />A first example:di-muon decays of Z bosons<br />9/2/2009<br />Physics Data Processing - Sander Klous<br />20<br />
  21. 21. Semi leptonic decays of top pairs<br />Tag on the jet side and probe on the muon side<br />Extract number of signal events (1 fb-1)<br />Invariant mass reconstruction on hadronic side<br />9/2/2009<br />Physics Data Processing - Sander Klous<br />21<br />Possible problem: contamination with tau to muon decays<br />
  22. 22. Event by event trigger efficiency<br />Determine individual object efficiencies<br />Extrapolate T&P from di-muon decays of Z-bosons<br />Combine objects into an event efficiency<br />Weigh the event appropriately <br />9/2/2009<br />Physics Data Processing - Sander Klous<br />22<br />Correct isolation withrespect to closest jet<br />Correct foreta dependency<br />Without corrections<br />Tamsett – Royal Holloway<br />
  23. 23. Turning the probe around<br />More complicated<br />The jets are multi-object triggers,e.g. 4J_95: 4 jets above 95 GeV<br />Thresholds are likely to change<br />The missing link:<br />Trigger efficiency of Semi Leptonic decays<br />Fully Hadronic decays of top quark pairs<br />Is it possible?<br />If not, can we do better?<br />9/2/2009<br />Physics Data Processing - Sander Klous<br />23<br />
  24. 24. Topology triggers and remote online farms<br />Topology triggers<br />Time consuming<br />Invariant masses<br />Likelihood fits<br />Budget in point 1<br />1500 machines x 8 cores per machine = 12000 cores<br />3 kHz EF input rate from LVL2<br />12000 / 3000 = 4 seconds per event (all triggers)<br />9/2/2009<br />Physics Data Processing - Sander Klous<br />24<br />Xin Wu<br />Event Filter<br />Mean: 1.57 seconds<br />(Erik van der Kraaij)<br />
  25. 25. Conclusion<br />Remote Online Farms<br />An interesting research topic<br />Several farms already in production<br />Still far from enabling grid for online processing<br />Physics Use Cases<br />Most likely complicated topologies<br />Fully hadronic decays of top quark pairs<br />Studies ongoing to understand trigger efficiencies<br />The future of grid is in the online connection<br />9/2/2009<br />Physics Data Processing - Sander Klous<br />25<br />

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